Analysis Combining Correlated Glaucoma Traits Identifies Five New Risk Loci for Open-Angle Glaucoma

Analysis combining correlated glaucoma traits identifies five new risk loci for open-angle glaucoma

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Citation Gharahkhani, P., K. P. Burdon, J. N. Cooke Bailey, A. W. Hewitt, M. H. Law, L. R. Pasquale, J. H. Kang, et al. 2018. “Analysis combining correlated glaucoma traits identifies five new risk loci for open- angle glaucoma.” Scientific Reports 8 (1): 3124. doi:10.1038/ s41598-018-20435-9. http://dx.doi.org/10.1038/s41598-018-20435-9.

Published Version doi:10.1038/s41598-018-20435-9

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OPEN Analysis combining correlated glaucoma traits identifes fve new risk loci for open-angle glaucoma Received: 23 August 2017 Puya Gharahkhani 1, Kathryn P. Burdon 2, Jessica N. Cooke Bailey 3, Alex W. Hewitt 2, Accepted: 18 January 2018 Matthew H. Law 1, Louis R. Pasquale 4,5, Jae H. Kang 5, Jonathan L. Haines3, Published: xx xx xxxx Emmanuelle Souzeau 6, Tiger Zhou6, Owen M. Siggs 6, John Landers6, Mona Awadalla6, Shiwani Sharma6, Richard A. Mills6, Bronwyn Ridge6, David Lynn7, Robert Casson8, Stuart L. Graham9, Ivan Goldberg10, Andrew White10,11, Paul R. Healey10,11, John Grigg10, Mitchell Lawlor10, Paul Mitchell11, Jonathan Ruddle12, Michael Coote12, Mark Walland12, Stephen Best13, Andrea Vincent13, Jesse Gale14, Graham RadfordSmith1,15, David C. Whiteman1, Grant W. Montgomery1,16, Nicholas G. Martin1, David A Mackey2,17, Janey L. Wiggs4, Stuart MacGregor1, Jamie E. Craig6 & The NEIGHBORHOOD consortium* Open-angle glaucoma (OAG) is a major cause of blindness worldwide. To identify new risk loci for OAG, we performed a genome-wide association study in 3,071 OAG cases and 6,750 unscreened controls, and meta-analysed the results with GWAS data for intraocular pressure (IOP) and optic disc parameters (the overall meta-analysis sample size varying between 32,000 to 48,000 participants), which are glaucoma-related traits. We identifed and independently validated four novel genome-wide signifcant associations within or near MYOF and CYP26A1, LINC02052 and CRYGS, LMX1B, and LMO7 using single variant tests, one additional locus (C9) using gene-based tests, and two genetic pathways - “response to fuid shear stress” and “abnormal retina morphology” - in pathway-based tests. Interestingly, some of the new risk loci contribute to risk of other genetically-correlated eye diseases including myopia and age-related macular degeneration. To our knowledge, this study is the frst integrative study to combine genetic data from OAG and its correlated traits to identify new risk variants and genetic pathways, highlighting the future potential of combining genetic data from genetically-correlated eye traits for the purpose of gene discovery and mapping.

OAG is characterized by optic nerve damage and progressive loss of peripheral vision, with many patients remain- ing undiagnosed until severe irreversible vision loss has occurred1,2. OAG has a signifcant genetic component

1QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. 2University of Tasmania, Hobart, Tasmania, Australia. 3Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, OH, USA. 4Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA. 5Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA. 6Department of Ophthalmology, Flinders University, Adelaide, South Australia, Australia. 7South Australian Health & Medical Research Institute, School of Medicine, Flinders University, Adelaide, South Australia, Australia. 8South Australian Institute of Ophthalmology, University of Adelaide, Adelaide, South Australia, Australia. 9Ophthalmology and Vision Science, Macquarie University, Sydney, New South Wales, Australia. 10Department of Ophthalmology, University of Sydney, Sydney, Australia. 11Centre for Vision Research, The Westmead Institute for Medical Research, University of Sydney, Westmead, NSW, Australia. 12Centre for Eye Research Australia (CERA), University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia. 13Department of Ophthalmology, University of Auckland, Auckland, New Zealand. 14Department of Ophthalmology, University of Otago, Dunedin, Otago, New Zealand. 15School of Medicine, University of Queensland, Herston Campus, Brisbane, QLD, Australia. 16Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia. 17Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Australia. *A comprehensive list of consortium members appears at the end of the paper. Correspondence and requests for materials should be addressed to P.G. (email: [email protected]) or J.E.C. (email: [email protected])

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Meta-analysis Chr SNP Risk allele P-value Analysis heterogeneity P Nearest Genes 10 rs72815193 G 6.10 × 10−10 OAG + VCDR 0.31 MYOF and XRCC6P1 3 rs56962872 G 2.81 × 10−8 OAG + VCDR 0.51 LINC02052 and CRYGS 9 rs6478746 G 4.54 × 10−8 OAG + CA 0.44 LOC105376277 and LMX1B 1 rs148639588 T 3.53 × 10−8 OAG + CA 0.71 COL11A1

Table 1. Association results for the best SNPs within the genome-wide signifcant regions in meta-analyses of ANZRAG OAG and the endophenotypes. Efect sizes of these SNPs on OAG are presented in Table 2. OAG, open-angle glaucoma; CA, cup area; VCDR, vertical cup to disk ratio.

ANZRAG NEIGHBORHOOD combined Efect Other Chr SNP allele allele OR SE P-value OR SE P-value OR SE P-value 10 rs4918865^ C G 1.149 0.03 1.89 × 10−5 1.086 0.04 0.01958 1.119 0.02 2.31 × 10−6 3 rs56962872 A G 0.862 0.04 2.91 × 10−5 0.892 0.04 0.002324 0.876 0.03 3.03 × 10−7 9 rs6478746 A G 0.853 0.04 1.09 × 10−5 0.909 0.04 0.01231 0.879 0.03 9.10 × 10−7 13 rs9530458 T C 1.158 0.03 4.50 × 10−6 1.138 0.03 0.00018 1.148 0.02 3.45 × 10−9

Table 2. GWAS statistics for the new OAG loci in ANZRAG (the discovery OAG set), NEIGHBORHOOD (the replication OAG set), and combined (fxed-efect meta-analysis). ^rs4918865 in high LD with rs72815193, LD r2 = 0.93; OR, odds ratio; SE, standard error of regression coefcent.

with a relative risk of over 9 in frst-degree relatives of afected individuals compared to relatives of unafected people3. Our previous genome-wide association studies (GWAS) have reproducibly identifed several risk loci for OAG including TMCO1, CDKN2B-AS1, SIX6, CAV1, CAV2, ABCA1, AFAP1, GMDS, ARHGEF12, TXNRD2, ATXN2, and FOXC14–9. However, the majority of the genetic variance contributing to OAG remains unexplained, emphasizing that further studies to identify additional risk loci for OAG are required in order to make genetic risk prediction more clinically useful. Optic disk parameters including cup area (CA; the central area), disc area (DA; the total area of optic disc including cup area and the surrounding area containing axons of the retinal ganglion cells), and vertical cup-disc ratio (VCDR; the ratio of the vertical diameter of cup area to the vertical diameter of the optic disc) are key measurements used to assess OAG diagnosis and progression10. Elevated intraocular pressure (IOP) is the major known risk factor for OAG2. We refer to CA, DA, VCDR, and IOP as OAG endophenotypes or quantitative traits. Tere are high genetic correlations between these quantitative traits and OAG, with several of the already known risk loci for these traits overlapping with each other and with OAG loci, demonstrating their utility as endo- phenotypes11–14. Tese fndings suggest that combining genetic data from OAG and its endophenotypes has the potential to increase the probability of identifying genetic variants that are common between traits, thus enabling the extraction of greater genetic power from valuable disease cohorts. In this study, we sought to identify additional risk loci contributing to OAG susceptibility by (1) increasing the sample size for OAG, (2) combining GWAS data from OAG and its endophenotypes in order to increase our statistical power to identify new risk loci for OAG, and (3) applying gene and pathway based approaches. Results In total, 3,071 OAG cases from the Australian & New Zealand Registry of Advanced Glaucoma (ANZRAG) obtained in three phases of data collection, and 6,750 unscreened controls of European descent were used as the GWAS discovery dataset in this study (Supplementary Table 1). Five loci were associated with OAG at genome-wide signifcance level in the meta-analysis of GWAS results between the three phases of ANZRAG data (P < 5 × 10−8), including regions near or within CDKN2B-AS1, ABCA1, C14orf39 and SIX6, TMCO1, and ARHGEF12, all of which are now well established risk loci for OAG5–7,9. Manhattan and Q-Q plots are shown in Supplementary Figure 1. Genomic infation factor lambda was 1.006 for this analysis. Next, to increase the power of this study to identify new risk loci for OAG, we performed GWAS meta-analyses of ANZRAG OAG and each of the endophenotypes (CA, DA, VCDR, and IOP) that we obtained from our previous study11 (Supplementary Table 1). Before performing the meta-analyses, we confrmed validity of the endophenotypes for OAG by showing that there were signifcant genetic correlations between OAG and the endophenotypes (ranging between 20% and 47%, Supplementary Table 2; p ≤ 0.018) using the cross-trait bivariate LD score regression approach15. By design, the endophenotype studies did not include any of the OAG cases. Tis was further confrmed in the LD score bivariate analyses where the intercepts were close to zero with 95% confdence intervals (CI) overlapping zero, indicating that there was not signifcant sample overlap between our OAG and the endophenotypes studies. Moreover, intercepts of the univariate LD score regression analyses16 were close to 1 with 95% CIs overlapping 1 (Supplementary Table 3), indicating that there was no model misspecifcation and other sources of bias such as population stratifcation and cryptic relatedness in either study16. Four genomic regions that were genome-wide signifcant in meta-analyses of ANZRAG OAG and one of the endophenotypes (Table 1), and were not previously known risk loci for OAG, and had at least P < 0.05 in the OAG separate analysis, were taken forward for validation. Te best SNPs within these regions were rs72815193[G]

SCIENTIFIC REPOrTS | (2018)8:3124 | DOI:10.1038/s41598-018-20435-9 2 www.nature.com/scientificreports/

P-value OAG P-value OAG Chr SNP (ANZRAG) (combined)* P-value CA P-value DA P-value VCDR P-value IOP Nearest gene 10 rs4918865^ 1.89 × 10−5 2.31 × 10−6 6.66 × 10−5 0.9062 2.10 × 10−5 0.3966 MYOF and XRCC6P1 3 rs56962872 2.91 × 10−5 3.03 × 10−7 0.000321 0.2832 0.000209 0.3793 LINC02052 and CRYGS 9 rs6478746 1.09 × 10−5 9.10 × 10−7 0.001659 0.6499 0.003575 0.03146 LOC105376277 and LMX1B 13 rs9530458 4.50 × 10−6 3.45 × 10−9 0.01305 0.2964 0.01631 0.04911 LMO7

Table 3. Association of the new loci with OAG and each of the endophenotypes separately. ^rs4918865 is in high LD with rs72815193, LD r2 = 0.93; *Meta-analysis of OAG in ANZRAG and NEIGHBORHOOD data; OAG, open-angle glaucoma; CA, cup area; DA, disk area; VCDR, vertical cup to disk ratio; IOP, intraocular pressure.

Figure 1. Regional plots for the new risk loci identifed in single variant analyses in this study. Te most signifcantly associated SNPs in each region are marked as solid purple diamonds. Pairwise correlations (LD r2) between the top SNP and the other SNPs in a 400 kb fanking region are illustrated by diferent colours. Blue spikes show estimated recombination rates. (a) rs72815193 on chromosome 10 near MYOF, CYP26A1, and CYP26C (the most signifcant results were obtained in combined OAG and VCDR analysis conducted in combined Asians and European ancestry). (b) rs56962872 on chromosome 3 within LOC253573 (LINC02052), near CRYGS and TBCCD1 (the most signifcant results were obtained in combined OAG and VCDR analysis conducted in European ancestry). (c) rs6478746 on chromosome nine near LMX1B (the most signifcant results were obtained in combined OAG and CA in European ancestry). (d) rs9530458 on chromosome 13 within LMO7 (combined OAG data from ANZRAG and NEIGHBORHOOD). cM = centimorgan.

(risk alleles are indicated within brackets) (P = 6.10 × 10−10) on chromosome 10 near MYOF, XRCC6P1, and CYP26A1 for combined OAG and VCDR (in European and Asian ancestries), rs56962872[G] (P = 2.81 × 10−8) on chromosome 3 within LINC02052 and near CRYGS for combined OAG and VCDR (in European ancestry), rs6478746[G] (P = 4.54 × 10−8) on chromosome 9 near LOC105376277 and LMX1B for combined OAG and CA (in European ancestry), and rs148639588[T] (P = 3.53 × 10−8) on chromosome 1 near COL11A1 for combined OAG and CA (in European ancestry). Of the above four loci that were genome-wide significant in our discovery meta-analyses, two loci (LOC105376277/LMX1B and LINC02052/CRYGS) were replicated (P < 0.0125, the Bonferroni-corrected threshold considering four independent tests) for OAG in an independent replication study, the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database (NEIGHBORHOOD) (Supplementary Notes), containing 3,853 OAG cases and 33,480 controls8 (Table 2). Furthermore, rs72815193 near MYOF and XRCC6P1 had a P = 0.06, and rs4918865 (in high LD with rs72815193; LD r2 = 0.93)

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NEIGHBORHOOD Genes P-value Tissue Analysis Ndiscovery Approach replication P-value Nreplication C9 4.93 × 10−7 NA OAG and IOP: European ancestry 187 fastBAT 0.04 87 FAM203A 2.70 × 10−8 Nerve_Tibial OAG and CA: European ancestry 5 MetaXcan 0.14 2 HERC4 1.06 × 10−9 Cells_Transformed_fbroblasts OAG and DA: European ancestry and Asians 1 MetaXcan 0.20 1 RNF26 2.6 × 106−9 Brain_Cerebellum OAG and IOP: European ancestry and Asians 5 MetaXcan 0.0004 5 OAG and VCDR: European ancestry and NPAS4 3.97 × 10−9 Uterus 2 MetaXcan 0.05 2 Asians CAPN1 1.02 × 10−8 Esophagus_Gastroesophageal_Junction OAG and VCDR: European ancestry 2 MetaXcan 0.12 2 DHRS7 1.00 × 10−6 brain OAG and CA: European ancestry and Asians 1 EUGENE 0.07 1 HLA-DQA2 1.00 × 10−6 brain OAG and DA: European ancestry 34 EUGENE 0.70 30 HLA-DQB1 1.00 × 10−6 brain OAG and DA: European ancestry 35 EUGENE 0.86 31

Table 4. Previously unreported genes that were genome-wide signifcant in gene-based approaches in the discovery datasets, with their corresponding results in the replication dataset (NEIGHBORHOOD). NA, Not Applicable (fastBat does use a tissue-specifc approach); OAG, open-angle glaucoma; CA, Cup Area; DA, Disc Area; VCDR, Vertical Cup to Disc Ratio; IOP, Intraocular Pressure; Ndiscovery, Number of SNP(s) used in the discovery set; Nreplication, Number of SNP(s) used in the replication set.

had a P = 0.02 for OAG in NEIGHBORHOOD. Although the SNPs near MYOF and XRCC6P1 did not pass the Bonferroni-corrected threshold of P < 0.0125, rs4918865 was more strongly associated with the high-tension glaucoma (HTG) subset (P = 0.003 for HTG vs. P = 0.37 for normal-tension glaucoma (NTG)) in NEIGHBORHOOD. The COL11A1 association was not replicated in NEIGHBORHOOD (P = 0.41 for rs148639588). Te statistics including efect sizes of the top SNPs within the three new replicated loci with OAG separately (the meta-analysed OAG data from ANZRAG and NEIGHBORHOOD studies, without including the endophenotype data) are summarized in Table 2. All of the three new replicated loci were associated with CA and VCDR at, at least, nominal signifcance (P < 0.05), while LMX1B was also nominally (P = 0.03) associated with IOP (Table 3). Manhattan and Q-Q plots are shown in Supplementary Figure 1, and regional association plots in Fig. 1. Genomic infation factor lambda ranged between 1.03 and 1.05 for these analyses. Next, results for the SNPs that were not genome-wide signifcant, but approached this threshold (SNPs with P < 1 × 10−7) in the ANZRAG OAG meta-analysis or the meta-analysis of OAG and its endophenotypes, were combined with those from the NEIGHBORHOOD replication data, using a fxed-efects meta-analysis. A fourth locus on chromosome 13 within LMO7 that was nearly genome-wide signifcant in the OAG and CA (European ancestry) meta-analysis (rs9530458 [T], P = 2.71 × 10−7) became genome-wide significant (rs9530458 [T], OR = 1.148, P = 3.45 × 10−9) in the meta-analysis of the OAG data (ANZRAG discovery and NEIGHBORHOOD replication studies), without including the endophenotypes. Tis SNP was nominally associated with CA, VCDR and IOP (Table 3). Regional association for this locus is plotted in Fig. 1. We also investigated association of the new loci with NTG and HTG subsets within the ANZRAG and NEIGHBORHOOD data (overall 1,546 NTG cases, 3,412 HTG cases, and 40,230 controls). Te results summarized in Supplementary Table 4 show that the 95% CIs overlap between the NTG and HTG analyses, suggesting that these loci may afect both NTG and HTG. However, larger sample sizes are required to further investigate this, as especially for NTG, the 95% CIs are quite wide, and for the LMO7 SNP (rs9530458) overlaps 1. We performed a series of sensitivity analyses by excluding the ANZRAG cases in which visual feld data was unavailable (585 people) as well as people with mixed-mechanism glaucoma (277 people with OAG as well as a secondary glaucoma) to ensure that the results were not driven by uncertainty in phenotype assign- ment. Te results from the sensitivity analyses in ANZRAG were meta-analysed with the endophenotype or NEIGHBORHOOD results as for the main analysis (Supplementary Tables 5 and 6). Overall, efect sizes obtained from the original and sensitivity analyses were similar, suggesting that our results were not biased by presence of any phenotype uncertainties. Interestingly, rs72815193 and rs4918865 within the MYOF and XRCC6P1 locus are in high LD (r2 = 0.840 and r2 = 0.9, respectively) with rs10882165, a SNP that has been shown to be associated with refractive error (P = 1 × 10−11)17, indicating that this locus may afect glaucoma and its endophenotypes as well as myopia. In addition, SNPs within LMO7 have been suggestively associated with corneal astigmatism (P = 4 × 10−6 for rs11841001)18. However, rs9530458 is in low LD (r2 = 0.14) with rs11841001 (P = 0.06 in the OAG and CA analysis in Europeans), suggesting that even if the LMO7 gene afects glaucoma as well as corneal astigmatism, this efect may come from independent risk variants within LMO7. On the other hand, the MYOF and XRCC6P1 locus is ~1 Mb away from PLCE1, a known risk locus for VCDR. However, rs72815193 (MYOF) is not in LD with rs7072574 (PLCE1) (LD r2 = 0.001) (P = 3.86 × 10−6 in the OAG and VCDR analysis in Europeans), suggesting that these are independent loci.

Gene-based results. We used the approaches implemented in MetaXcan19, fastBAT20, and EUGENE21, to identify genes whose genetic variants or expression levels were signifcantly associated with development of OAG and its endophenotypes. Tese gene-based tests are complementary since they make diferent assumptions and use diferent approaches and input data to identify associated genes. Afer Bonferroni correction for multiple testing (refer to the Methods section), nine genes (one from fastBAT, fve from MetaXcan, and three from Eugene approaches, Table 4) that were genome-wide signifcant in the gene-based methods, and were not overlapping with

SCIENTIFIC REPOrTS | (2018)8:3124 | DOI:10.1038/s41598-018-20435-9 4 www.nature.com/scientificreports/

the known risk loci for OAG and its endophenotypes, were taken forward for validation in NEIGHBORHOOD. For MetaXcan and EUGENE approaches, we investigated replication of the signifcant genes in the same tissues that showed signifcance in the discovery set since the results from these approaches are tissue-specifc. One previously unreported gene became gene-wide signifcant (P < 7 × 10−7; see the Methods section) in the fastBAT approach (Table 4). Tis gene, complement factor 9 (C9) (P = 4.93 × 10−7 in the combined OAG and IOP analysis in European ancestry) was replicated in the NEIGHBORHOOD data (P = 0.04). Te best result in the single variant tests for rs56345442, the top SNP within C9, was observed in the combined OAG and IOP analysis (P = 4.43 × 10−6 for combined European ancestry and Asians, and P = 9.998 × 10−6 in European ancestry), suggesting that a larger sample size would be required to detect this association at genome-wide signifcance threshold in the single variant analysis. Five previously unreported genes were gene-wide signifcant (P < 5 × 10−8; see the Methods section) in the MetaXcan approach (Table 4), all of which were located within 1 Mb of a previously known locus. Of those genes, association of two genes, RNF26 (P = 2.66 × 10−9 in the discovery set) and NPAS4 (P = 3.97 × 10−9 in the discovery set), were replicated in the NEIGHBORHOOD data (P = 0.0004 and P = 0.05 for RNF26 and NPAS4, respectively). However, four of the eQTL SNPs (rs1893261, rs11823300, rs61898351, and rs11217821) used by MetaXcan to impute the gene expression levels for RNF26 are in LD r2 = 0.3 with rs11827818, located within a previously known locus (near ARHGEF12) for IOP and OAG. Repeating the analysis without SNPs in LD r2 > 0.2 with rs11827818 (56 SNPs remained out of the original 60 SNPs) led to a non-signifcant result for this gene (P = 0.85). Similarly, excluding the eQTL SNPs in LD r2 > 0.2 with rs7931311, an already known locus near SCYL1 showed a non-signifcant association for NPAS4 (P = 0.82; twenty SNPs remained out of the original 25 SNPs for this analysis). Although these results are valuable for the purpose of fne-mapping of the previously known associations, they suggest that RNF26 and NPAS4 are not new risk loci for OAG, but are driven by the eQTL SNPs within the previously known loci. Tree previously unreported genes were gene-wide signifcant (P < 9 × 10−6; see the Methods section) in brain using the EUGENE approach. However, none were replicated in brain using the EUGENE approach applied to the NEIGHBORHOOD data. Despite this, while DHRS7 was associated at P = 0.07 in brain in NEIGHBORHOOD, there was a stronger association at P = 0.0006 in blood. Tese data suggestively support that DHRS7 may also be a risk locus for OAG. Accordingly, in addition to the risk loci identifed in the single variant analyses, our gene-based approaches identifed and validated C9 as an additional new risk locus for OAG. Te previously reported OAG loci that also passed the gene-wide signifcance threshold in the gene-based tests included TMEM136 in the MetaXcan approach, AFAP1, AFAP1-AS, ARHGEF12, and TXNRD2 in the EUGENE approach, and TMCO1, ABCA1, C9orf53, CDKN2A, CDKN2B, CDKN2B-AS1, ARHGEF12, TMEM136, SIX1, SIX4, SIX6, AFAP1, GMDS, CAV1, and CAV2 in the fastBAT approach (Supplementary Table 7). Tese data provide further support for these genes being the target genes within the previously reported risk loci for OAG.

Pathway-based results. Two genetic pathways survived the signifcance threshold of P < 1 × 10−6 and false discovery rate < 0.05 in the pathway-based analysis in DEPICT22. One pathway was the “response to fuid shear stress” (GO: 0034405, P = 2.09 × 10−7, FDR < 0.01) in the combined OAG and CA analysis, and the other was “abnormal retina morphology” (MP: 0001325, P = 2.50 × 10−7, FDR < 0.01) in the combined OAG and VCDR analysis. Te “abnormal retina morphology” pathway is interesting because it emphasizes that common risk loci between OAG and VCDR could be functioning through mechanisms related to retinal formation.

Gene expression. We also investigated the expression of the nearest genes to the best associated SNPs within the new OAG risk loci using RNA sequencing data from relevant human tissues including optic nerve, optic nerve head, retina, ciliary body pars plicata, trabecular meshwork, corneal endothelium, corneal stroma, and corneal epithelium (see Methods section). We observed a higher expression of LMX1B in trabecular meshwork, corneal endothelium, and corneal stroma, MYOF in trabecular meshwork and corneal epithelium, and LMO7 in corneal epithelium (Supplementary Figure 2). A relatively higher expression of LMX1B and MYOF in the trabecular meshwork is interesting because it is consistent with the previous observations for other known OAG genes such as MYOC showing high expression profle in the trabecular meshwork23. In addition, the LMX1B results are also consistent with the results from the GTEx eQTL studies where rs4837100, the SNP in high LD with the top variant in the LMX1B locus is an eQTL (P = 7 × 10−5) for LMX1B in sub-cutaneous adipose tissues24, suggesting that risk variants within this locus may alter expression levels of LMX1B. Discussion Our study identifed four new OAG risk loci in single variant analyses as well as an additional locus using a gene-based approach. Interestingly, some of these new risk loci contribute to risk of other partially correlated eye diseases including age-related macular degeneration (AMD) and myopia (more details below). We also highlighted two genetic pathways associated with the development of OAG, one of which is gene-sets contributing to morphology of retina. Tis study highlights the potential of combining genetic data from correlated eye traits for the purpose of gene discovery and mapping. We showed that meta-analysis of GWAS summary statistics from OAG and its correlated traits (VCDR, CA, DA, and IOP) is capable of identifying new risk loci by increasing statistical power. To our knowledge, this is the frst study to use an integrative approach for OAG and its endophenotypes to identify new risk loci for OAG. Tis approach identifes risk variants common between OAG and its correlated traits, while increasing statistical power to detect variants with small efect sizes at the genome-wide signifcance threshold, which otherwise requires a much larger OAG sample for successful detection.

SCIENTIFIC REPOrTS | (2018)8:3124 | DOI:10.1038/s41598-018-20435-9 5 www.nature.com/scientificreports/

While all the new risk loci were at least nominally associated with CA and VCDR, none were associated with DA. Tis suggests that the new loci identifed in this study are more likely to infuence the size of the central area of the optic disk, rather than the total disc size. Tere have been some debates on whether the total size of the optic disc is a suitable trait to predict OAG risk and progression25. In this study we estimated a much smaller genetic correlation between DA and OAG as compared with genetic correlation between OAG and CA, VCDR, and IOP. In addition, the majority of the genome-wide signifcant loci in our meta-analyses of OAG and DA showed signifcant heterogeneity (P < 0.05) between the GWAS results from OAG and DA (data not shown), further suggesting that DA may not be as suitable as CA and VCDR to be used as an endophenotype for POAG. Bioinformatics functional features of the newly identifed risk loci or variants in high LD (r2 > 0.8) with them are discussed in Supplementary Materials. Tese loci are either quantitative trait loci that regulate the expression of genes within the regions, change sequence motifs for protein binding sites, or are located within DNAase hypersensitivity regions and within regions with enhancer or promoter motifs (Supplementary discussion). rs72815193 is an intergenic SNP on chromosome 10 and is located near several genes including XRCC6P1, MYOF, CYP26A1, CYP26C1, and EXOC6. MYOF encodes a calcium/phospholipid-binding protein that plays a role in membrane repair of endothelial cells damaged by mechanical stress (http://www.genecards.org/cgi-bin/ carddisp.pl?gene=MYOF). CYP26A1 and CYP26C1 are involved in regulation of cellular retinoic acid metabolism, eye development, and maturation of vision function by their efect on retina and retinal ganglion cells during the later stages of eye development26–28. Interestingly, microdeletion of approximately 363 kb within this region of chromosome 10, which included CYP26A1, CYP26C1, and EXOC6, was reported in three patients afected by non-syndromic bilateral and unilateral optic nerve aplasia in a Belgian pedigree29. Moreover, this locus is also a risk locus for refractive error, where rs10882165 (P = 1 × 10−11 for refractive error) is in high LD with rs72815193 (r2 = 0.84), the top risk SNP within this locus17. XRCC6P1 is a pseudogene with limited data available on function of this gene or its relevance to diseases. rs56962872 on chromosome 3 is an intronic variant within the LOC253573 (LINC02052) gene, near CRYGS and TBCCD1. LINC02052 is highly expressed in retina and vitreous humor CRYGS is a member of the crystallin gene families, which are expressed in human lens, retina, and cornea30. Mutations in CRYGS are associated with autosomal dominant paediatric cortical cataract in humans31. TBCCD1 is a centrosomal protein that plays a role in the regulation of centrosome and Golgi apparatus positioning, with consequences on cell shape and cell migration32. Interestingly, human XRP2 is a TBCC-domain containing protein mutated in certain forms of retinitis pigmentosa, a retinal degenerative disease33,34. Tus, TBCC-domain containing proteins including TBCCD1 may play a role in OAG through their efect on retinal formation or mechanisms such as cell shape and function. rs6478746 on chromosome 9 is located near LMX1B and LOC105376277. LMX1B is mutated in Nail-Patella Syndrome, characterized by nail, patella and elbow dysplasia, in which some patients develop OAG35. In support of this, a mouse model study showed that a dominant-negative mutation of Lmx1b causes glaucoma36. Tis gene is required for murine trabecular meshwork formation and thus has an important role in controlling IOP37, suggesting that this gene may infuence OAG through the mechanisms related to increased eye pressure. In support of this, rs6478746 was nominally associated with IOP (P = 0.03), and associated at P = 6.38 × 10−7 in the combined OAG and IOP analysis in European ancestry. rs9530458 is an intronic variant within LMO7, a protein-coding gene that may be involved in protein-protein interaction (http://www.genecards.org/cgi-bin/carddisp.pl?gene=LMO7&keywords=LMO7). An engineered 800 kilobase deletion of Uchl3 and Lmo7 caused defects in viability, postnatal growth and degeneration of muscle and retina in mice38. In addition, LMO7 has been suggestively (P = 4 × 10−6 for rs11841001) associated with corneal astigmatism. However, rs9530458 is not in high LD with the top corneal astigmatism SNP (rs11841001, LD r2 = 0.14, P = 0.06 in the OAG and CA analysis in Europeans), suggesting that independent variants within this gene may be involved in the development of OAG and corneal astigmatism. C9, the gene identifed in the gene-based approaches in this study also has interesting implications for OAG. Tis gene is one component of the complement system, a part of the innate immune response whose deregulation is considered to have a major role in pathogenesis of AMD39. Common and rare variants in multiple complement genes including C9 have been associated with AMD40–42, consistent with studies showing signifcant genetic correlation between AMD and glaucoma43. Moreover, there is some evidence that the complement system including C9 is activated in glaucomatous optic nerve head astrocytes44, suggesting a possible role of C9 in the development of OAG. Tis study has several limitations. First, we did not use the recently proposed approaches for meta-analysis of correlated traits using GWAS summary statistics45 which adjust for overlapping or related subjects, population stratifcation, and heterogeneity of efect between studies. In our study, as confrmed with the LD score regression analyses, we did not have biases such as population stratifcation and sample overlap between the OAG and endophenotype studies. Tus, we did not use the proposed approaches that adjust for such biases in this study. In addition, approaches such as that proposed by Zhu and colleagues are susceptible to detecting association for a trait that is mainly contributed to via only a subset of the traits. Although our approach has a similar limitation, we investigated the heterogeneity of association between studies to ensure that the results were not biased towards one study. Another limitation of this study is that we performed the combined analysis of OAG and each endophenotype separately, rather than including all the endophenotypes in the same analysis. Tis was justifed due to two reasons: (1) IOP and VCDR loci act through two distinct pathways (intraocular pressure vs optic disc morphology), and (2) the GWAS results for the endophenotypes included in this study were obtained from the same consortia study11, and thus the subjects overlap substantially between these phenotypes. In conclusion, this study highlighted several novel genes and cellular pathways likely to be involved in the development of OAG. Fine-mapping and functional validation of the new risk loci will help to better understand disease pathophysiology. Identifcation of additional risk loci using larger sample sizes in the future may lead to more accurate genetic risk prediction algorithms for OAG as well as identifcation of new molecular targets for prevention and intervention strategies.

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Methods Study design and participants. In total 3,071 OAG cases from the Australian & New Zealand Registry of Advanced Glaucoma (ANZRAG)46, and 6,750 unscreened controls of European descent were included in this study. Tis dataset involves three phases of OAG data collection, and hence, quality control (QC), imputation, and association analysis were conducted separately for each phase before combining the results in a meta-analysis. Te frst phase was previously published and comprises 1,155 advanced OAG cases and 1,992 controls genotyped on Illumina Omni1M or OmniExpress arrays (Illumina, San Diego, California, USA)7. Te second phase includes a further 579 advanced OAG cases genotyped on Illumina HumanCoreExome array and 946 controls selected from parents of twins previously genotyped on the same array. Te third phase comprises 1,337 OAG cases (11 advanced, 741 non-advanced, and 585 cases with visual feld data unavailable) genotyped on Illumina HumanCoreExome array and 3,812 controls selected from a study of endometriosis previously genotyped on the same array. Te diagnostic criteria have been described previously7. Te Approval was obtained from the Human Research Ethics Committees of Southern Adelaide Health Service/Flinders University, University of Tasmania, QIMR Berghofer Medical Research Institute and the Royal Victorian Eye and Ear Hospital. Written informed consent was obtained from all participants. All the methods were carried out in accordance with relevant guide- lines and regulations for human subject research, in accordance with the Declaration of Helsinki. For the endophenotypes we used GWAS results from our previously published data, which includes varying numbers of participants for each trait; between 22,000 and 24,000 Europeans, and between 7,000 and 9,000 Asians11 (Supplementary Table 1). By design, the endophenotype studies did not include any of the OAG cases. We combined the OAG GWAS results with the results obtained from the endophenotype GWASs in Europeans in the primary analysis as well as those obtained from combined European and Asian endophenotype GWASs in a secondary analysis. Te QC, imputation, and association testing has been previously described for these stud- ies11 as well as for the frst phase of the ANZRAG OAG study7– specifcally imputation was done using the 1000 Genomes Phase 1 Europeans reference panel. Te following paragraphs provide this information for the second and third phases of the ANZRAG OAG dataset.

Quality Control (QC). We used the same QC protocol as was used for the frst phase of the ANZRAG OAG GWAS. Briefy, we performed QC using PLINK 1.947,48 by removing individuals with more than 3% missing genotypes, and SNPs with call rate less than 97%, minor allele frequency (MAF) < 0.01, and Hardy-Weinberg equilibrium P < 0.0001 in controls and P < 5 × 10−10 in cases. Te same QC protocol was used for case and control datasets before merging to avoid mismatches between the merged datasets. We used PLINK1.9 to compute identity by descent based on autosomal markers, with one of each pair of individuals with relatedness of greater than 0.2 removed within each phase of the ANZRAG data as well as between the three phases. PLINK 1.9 was used to compute principal components for all participants and reference samples of known northern European ancestry (1000 Genomes British, CEU, Finland participants). Participants with PC1 or PC2 values > 6 standard deviations from the mean of known northern European ancestry group were excluded.

Imputation. Phasing of the genotyped SNPs was conducted using ShapeIT49 and imputation was performed using Minimac3 through the Michigan Imputation Server50, with the Haplotype Reference Consortium (HRC)51 r1.1 as the reference panel. SNPs with imputation quality (r2) > 0.3 and MAF > 0.01 were carried forward for analysis.

Association testing. We assessed associations between SNPs and OAG status adjusted for sex and the frst six principal components under an additive genetic model using the dosage scores obtained from imputation. Association analysis was performed either using SNPTEST v2.552,53 or PLINK 1.9. Genomic infation factor lambda was calculated to investigate the presence of infation due to model miss-specifcation or population stratifcation. We also performed a sensitivity analysis by excluding the OAG cases in which visual feld data was unavailable to ensure that the association results were not driven by including those people in the analysis. Similarly, people with mixed-mechanism glaucoma (277 people with OAG as well as a secondary glaucoma) were excluded in a sensitivity analysis as a further robustness check. Association of the top loci were also investigated in NTG and HTG subsets within the ANZRAG dataset (821 NTG cases, 1,544 HTG cases, and 6,750 controls). To increase the power of this study to identify new risk loci for OAG, we meta-analysed the OAG GWAS results with those obtained from the endophenotype GWASs. To confrm the validity of the endophenotypes for OAG, we estimated genetic correlation between OAG and the endophenotypes using the cross-trait bivariate LD score regression approach15. Tis approach estimates genetic correlation between traits from regression of the combined Z scores of each SNP for two traits obtained from GWAS summary statistics on LD scores calculated from a reference panel. LD scores are incorporated in estimation of genetic correlation based on the fact that SNPs with high LD have, on average, higher chi-square statistics for association with a trait as compared with SNPs with low LD. In addition, an intercept close to zero in these analyses indicates that there is not a signifcant sample overlap between studies. Moreover, we used the univariate LD score regression approach16 to investigate presence of model or structural bias in the OAG and endophenotype GWAS data. An intercept close to 1 in a univariate analysis indicates that there is no model misspecifcation and other sources of bias such as population stratifcation and cryptic relatedness16. Meta-analysis of the ANZRAG OAG results between the three phases was performed in METAL54 using the fxed-efects inverse-variance weighting approach using SNP efect sizes and their standard errors. In addition, the quantitative trait GWAS results were meta-analysed with the ANZRAG OAG GWAS using the P-value approach in METAL. In this approach, Z scores are created for each SNP from P-values and direction of efect for tested alleles, and combined as weighted sum of the individual statistics where the weights are proportional to the

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square root of the number of individuals examined in each study. Genomic control correction was applied to each GWAS dataset prior to the meta-analysis to ensure that infation was not driving our results. We also investigated the heterogeneity of Z scores between studies using the approach implemented in METAL. Q-Q and Manhattan plots were created in R. For the purpose of creating these plots, we excluded genome-wide signifcant SNPs that showed heterogeneity of efect (Cochran’s Q Test P < 0.05) between OAG and the quantitative traits that included Asians. Regional association plots were created using LocusZoom55. SNPs with P < 1 × 10−7 from the overall meta-analysed results that were previously unreported for OAG, and were at least nominally associated (P < 0.05) with OAG in the combined OAG and the quantitative trait analyses, were taken forward for validation in an independent US dataset, the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database (NEIGHBORHOOD), containing 3,853 OAG cases and 33,480 controls8. More details on the NEIGHBORHOOD study has been provided in the Supplementary Notes.

Gene-based tests. Gene-based tests were conducted using the approaches implemented in MetaXcan19 fastBAT20, and EUGENE21. We used the GWAS results from OAG as well as combined OAG and its endophenotypes for the gene-based tests. MetaXcan is an extension of PredixCan56, a gene-based approach that uses GWAS summary results to impute the genetic component of gene expression in diferent tissues (thus eliminating the need to directly measure gene expression levels), and correlates the imputed gene expressions with phenotypes of interest. Te Bonferroni-corrected threshold for multiple testing was set to 5 × 10−8, considering the maximum number of 7,230 genes tested in 44 tissues for three traits, OAG, IOP, and VCDR (note that VCDR is the ratio of CA to DA, so highly correlated with these traits). Te MetaXcan method is developed based on the publically available European reference data; however, this method is quite robust to ethnicity diferences19. Tus, we ran the MetaXcan analyses using European ancestry as well as combined Asians and European ancestry data. Te combined ethnicity dataset was >80% European. fastBAT (fast and fexible set-Based Association Test) is a gene-based approach that calculates the association p-values for a set of SNPs (within ±50 Kb of a gene for this study) using GWAS summary data while accounting for LD between SNPs. Te Bonferroni-corrected signifcance threshold was set to 7 × 10−7, considering the maximum number of 24,654 genes tested for three traits. We ran the fastBAT analyses using European ancestry and Asians data separately, and combined P-values using the sum of Z scores method. In addition, we also used the combined Asians and European ancestry meta-analysis results as input for this analysis. EUGENE is a gene-based approach that captures the aggregate efects of independent eQTL SNPs (both cis-acting and trans-acting) for each gene using GWAS summary statistics. Te most suitable tissue for OAG that is available to use with the EUGENE approach is the brain tissue. Considering the maximum number of 5,487 genes tested in brain for three traits, the Bonferroni-corrected threshold was set to 9 × 10−6. Since the current version of EUGENE is developed based on publically available European reference data, we ran the EUGENE analyses using the meta-analysis results from subjects with European ancestry only. However, since the combined ethnicity analyses comprised mainly (at least 80%) Europeans, we also ran these analyses using combined Asian and European ancestry data.

Pathway-based tests. We used the results from the ANZRAG OAG meta-analysis as well as the meta-analysis of ANZRAG OAG and its endophenotypes to do a pathway analysis using the approach implemented in DEPICT22. Although it is preferable to use genome-wide signifcant loci for DEPICT provided there are at least 10 independent risk loci available for a trait, because we did not have this many independent genome-wide signifcant loci for each of the meta-analyses we used SNPs with P < 1 × 10−7 for the pathway analyses. Due to the polygenic nature of the studied traits, as well as our relatively low statistical power to detect SNPs with small efect sizes, including more associated SNPs in the analysis may result in improved power to detect associated pathways. Assuming that all the 14,463 pathways used by DEPICT are independent, and considering testing those pathways for three traits, we set the Bonferroni-corrected signifcance threshold to P < 1 × 10−6 and false discovery rate <0.05.

Gene expression. Ocular tissues of interest (corneal epithelium, corneal stroma, corneal endothelium, trabecular meshwork, pars plicata of the ciliary body, retina, optic nerve head and optic nerve) were collected from donor human eyes within 24 hours post-mortem (mean = 9.7 ± 5.3 hours) and fxed in RNAlater. RNA quality was assessed using Agilent Bioanalyzer 2100 RNA 6000 Nano Assay (Catalog #G2938C, Santa Clara, USA) (mean RNA integrity number = 6.5 ± 1.8) and concentrations were quantifed on the Qubit 2.0 Fluorometer (Catalog #Q32866, Carlsbad, USA) using Qubit RNA Assay Kits (Catalog #Q32852, Carlsbad,® USA). 250 nanograms of total RNA from each tissue sample was ™indexed using Bioo Scientifc NEXTfex Rapid Directional mRNA-Seq Kit Bundle with RNA-Seq Barcodes and poly(A) beads (Catalog #5138-10,® Austin,™ Texas) and sequenced on the Illumina NextSeq 500 using High Output v2 Kit (75 cycles) (Catalog #FC-404-2005, San Diego, USA). All raw sequences were quality-control® fltered and trimmed with Trimgalore v0.4.0 (http://www.bioinformatics.babra- ham.ac.uk/projects/trim_galore/), aligned to the human genome (GRCh38 assembly) using TopHat v2.1.157,58 and normalized using the trimmed mean of M-values (TMM) normalisation method59 in Bioconductor R package EdgeR v3.10.260. Gene diferential expression was analysed using EdgeR sofware with Benjamini Hochberg false-positive adjustment61.

In silico functional analyses. Bioinformatics functional analyses were performed for the novel genome-wide significant loci using HaploReg62, RegulomeDB63, ENCODE Project Consortium64, and eQTL-browsers including Blood eQTL-Browser65 and GTEx-Browser24. Te top SNP in each locus as well as those with LD r2 > 0.8 with the top SNPs were used for these analyses.

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Acknowledgements ANZRAG: Support for recruitment of ANZRAG was provided by the Royal Australian and New Zealand College of Ophthalmology (RANZCO) Eye Foundation. Genotyping was funded by the National Health and Medical Research Council of Australia (#535074 and #1023911). Tis work was also supported by funding from NHMRC #1031362 awarded to J.E.C., NHMRC #1037838 awarded to A.W.H., NHMRC #1048037 awarded to S.L.G., NHMRC #1009844 awarded to R.J.C. and I.G., NHMRC #1031920 and Alcon Research Institute grant awarded to D.A.M., Allergan Unrestricted grant awarded to A.J.W., and the BrightFocus Foundation and a Ramaciotti Establishment Grant. Te authors acknowledge the support of Ms Bronwyn Usher-Ridge in patient recruitment and data collection, and Dr Patrick Danoy and Dr Johanna Hadler for genotyping. Controls for the ANZRAG cohort were drawn from the Australian Cancer Study, the Study of Digestive Health, a study of infammatory bowel diseases, a study of endometriosis, and QIMR Berghofer twin study. Te Australian Cancer Study was supported by the Queensland Cancer Fund and the National Health and Medical Research Council (NHMRC) of Australia (Program no. 199600, awarded to David C. Whiteman, Adele C. Green, Nicholas K. Hayward, Peter G. Parsons, David M. Purdie, and Penelope M. Webb; and program no. 552429 awarded to David C. Whiteman). Te Study of Digestive Health was supported by grant number 5 R01 CA 001833 from the US National Cancer Institute (awarded to David C. Whiteman). Te Barrett’s and Esophageal Adenocarcinoma Genetic Susceptibility Study (BEAGESS) sponsored the genotyping of oesophageal cancer and Barrett’s oesophagus cases, which were used as unscreened controls in the ANZRAG cohort. BEAGESS was funded by grant R01 CA136725 from the US National

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Cancer Institute. Genotyping for part of the Australian twin control samples included in the ANZRAG cohort was funded by an NHMRC Medical Genomics Grant. Genotyping for the remainder of twin controls was performed by the National Institutes of Health (NIH) Center for Inherited Research (CIDR) as part of an NIH/National Eye Institute (NEI) grant 1RO1EY018246, and we are grateful to Dr Camilla Day and staf. We acknowledge with appreciation all women who participated in the QIMR Berghofer endometriosis study. We thank Endometriosis Associations for supporting study recruitment. We thank Sullivan Nicolaides and Queensland Medical Laboratory for pro bono collection and delivery of blood samples and other pathology services for assistance with blood collection. Te QIMR twin and endometriosis studies were supported by grants from the National Health and Medical Research Council (NHMRC) of Australia (241944, 339462, 389927,389875, 389891, 389892, 389938, 443036, 442915, 442981, 496610, 496739, 552485, 552498, 1049472 and 1050208), the Cooperative Research Centre for Discovery of Genes for Common Human Diseases (CRC), Cerylid Biosciences (Melbourne), and donations from Neville and Shirley Hawkins. We thank Matthew A. Brown, Margaret J. Wright, Megan J. Campbell, Anthony Caracella, Scott Gordon, Dale R Nyholt, Anjali K Henders, B. Haddon, D. Smyth, H. Beeby, O. Zheng, B. Chapman for their input into project management, databases, sample processing, and genotyping. We are grateful to the many research assistants and interviewers for assistance with the studies contributing to the QIMR Berghofer twin collection. NEIGHBORHOOD: The NEIGHBORHOOD data collection and analysis is supported by NIH/NEI R01EY022305 (JL Wiggs) and NIH/NEI P30 EY014104 (JL Wiggs). Support for collection of cases, controls and analysis for individual datasets is as follows. Genotyping services for the NEIGHBOR study were provided by the Center for Inherited Disease Research (CIDR) and were supported by the National Eye Institute through grant HG005259-01 (JL Wiggs). Genotyping for the MEEI dataset and some NHS and HPFS cases (GLAUGEN) was completed at the Broad Institute and supported by GENEVA project grant HG004728 (LR Pasquale) and U01-HG004424 (Broad Institute). Genotype data cleaning and analysis for the GLAUGEN study was supported by U01HG004446 (C Laurie). Collecting and processing samples for the NEIGHBOR dataset was supported by the National Eye Institute through ARRA grants 3R01EY015872-05S1 (JL Wiggs) and 3R01EY019126-02S1 (MA Hauser). Funding for the collection of NEIGHBOR cases and controls was provided by NIH grants: EY015543 (RR Allingham), EY006827 (D Gaasterland); HL73042, HL073389, EY13315 (MA Hauser); CA87969, CA49449, UM1 CA186107, UM1 CA167552, EY009149 (PR Lichter), HG004608 (C McCarty), EY008208 (FA Medeiros), EY015473 (LR Pasquale), EY012118 (M Pericak-Vance), EY015682 (A Realini), EY011671 (JE Richards), EY09580 (JE Richards), EY013178 (JS Schuman), RR015574, EY015872 (JL Wiggs), EY010886 (JL Wiggs), EY009847 (JL Wiggs), EY011008,EY144428 (K Zhang), EY144448 (K Zhang), EY18660 (K Zhang). Te collection of Marshfeld clinic cases and controls was supported by 1U02HG004608-01, 5U01HG006389-02 and NCATS/NIH grant UL1TR000427. In addition some NHS/HPFS cases and controls and analysis of GWAS data was supported by R01 CA131332. Te WGHS is supported by HL043851 and HL080467 from the National Heart, Lung, and Blood Institute and CA047988 from the National Cancer Institute, the Donald W. Reynolds Foundation and the Fondation Leducq, with collaborative scientifc support and funding for genotyping provided by Amgen. POAG case identifcation in WGHS was supported by 3R01 EY15473-5S1 (LR Pasquale). JL Wiggs and LR Pasquale are supported by the Harvard Glaucoma Center for Excellence and an unrestricted grant from Research to Prevent Blindness. Dr. Pasquale is also supported by a Harvard Medical School Distinguished Scholar award. Author Contributions P.G., K.P.B., S. MacGregor, and J.E.C. were involved in designing the study. A.W.H., L.R.P., G.W.M., N.G.M., G.R.S., D.C.W., J.L.W., D.A.M., P.M. and J.E.C. were involved in participant recruitment, sample collection or genotyping. Analysis was performed by P.G., K.P.B., S.S., E.S., T.Z., O.M.S., D.L., M.H.L., J.N.C.B., L.R.P., J.H.K., J.L.H., J.L.W., and S. MacGregor. Clinician assessments were performed by R.A.M., D.A.M. J.L., M.A., B.R., R.C., S.L.G., I.G., A.W., P.R.H., J.G., J.Gale, M.L., P.M., J.R., M.C., M.W., S.B., A.V., J.Gale, and J.E.C. Te initial draf was written by P.G., S. MacGregor, and J.E.C. Additional Information Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-20435-9. Competing Interests: Te authors declare no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Consortia The NEIGHBORHOOD consortium R. Rand Allingham18, Murray Brilliant19, Donald L. Budenz20, John H. Fingert21,22, Douglas Gaasterland23, Teresa Gaasterland24, Lisa Hark25, Michael Hauser18,26, Robert P. Igo Jr27, Peter Kraft28,29, Richard K. Lee30, Paul R. Lichter31, Yutao Liu32,33, Syoko Moroi31, Margaret Pericak-Vance34, Anthony Realini35, Doug Rhee36, Julia E. Richards31,37, Robert Ritch38, Joel S. Schuman39, William K. Scott34, Kuldev Singh40, Arthur J. Sit41, Douglas Vollrath42, Gadi Wollstein39 & Donald J. Zack43

18Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA. 19Center for Human Genetics, Marshfeld Clinic Research Foundation, Marshfeld, WI, USA. 20Department of Ophthalmology, University of North Carolina, Chapel Hill, NC, USA. 21Department of Ophthalmology, University of Iowa, College of Medicine, Iowa City, IA, USA. 22Department of Anatomy and Cell Biology, University of Iowa, College of Medicine, Iowa City, IA, USA. 23Eye Doctors of Washington, Chevy Chase, MD, USA. 24Scripps Genome Center, University of California at San Diego, San Diego, CA, USA. 25Wills Eye Hospital, Glaucoma Research Center, Philadelphia, PA, USA. 26Department of Medicine, Duke University Medical Center, Durham, NC, USA. 27Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA. 28Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA. 29Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, MA, USA. 30Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA. 31Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA. 32Department of Cellular Biology and Anatomy, Georgia Regents University, Augusta, GA, USA. 33James & Jean Culver Vision Discovery Institute, Georgia Regents University, Augusta, GA, USA. 34Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA. 35Department of Ophthalmology, West Virginia University Eye Institute, Morgantown, WV, USA. 36Department of Ophthalmology and Visual Sciences, UH Cleveland Medical Center, Cleveland, OH, USA. 37Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA. 38Einhorn Clinical Research Center, Department of Ophthalmology, New York Eye and Ear Infrmary of Mt. Sinai, New York, NY, USA. 39Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, USA. 40Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA, USA. 41Department of Ophthalmology, Mayo Clinic, Rochester, MN, USA. 42Department of Genetics, Stanford University School of Medicine, Palo Alto, CA, USA. 43Wilmer Eye Institute, Johns Hopkins University Hospital, Baltimore, MD, USA.

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